This folder will generally not help interpret the results of the analysis, but contains important supporting files for other uses. Within the same top level folder, there is a folder called supporting_files. This helps improve traceability of results packages that get downloaded off of Cytobank onto local file systems. ![]() This file contains essential attributes of the run including basic metadata such as which user executed the run, what settings were used, and URLs back to the setup page in Cytobank. Within that folder, there is FlowSOM run info file which specifies the run info that is associated with this particular analysis and settings used for the run as references. Click on Download the run info and plots to download FlowSOM_test_results folder. From the setup page, you can View created experiment or Download run info and Plots. You can find your analysis from the email link, from the setup page for the FlowSOM run within the original experiment (navigate here via the Experiment Summary page or the Advanced Analyses menu), and in the Attachments section of the FlowSOM analysis experiment. You will receive an email notification once the analysis is completed. Assess differences between groups of quality control by concatenating samples.Assess differences between groups or quality control with contour plots.Color heatmaps and dot plots by functional markers.Color FlowSOM metaclusters by clustering channels.Heatmap view of flowSOM metaclusters by clustering channel.Overlay FlowSOM metaclusters on a viSNE map.Interacting with FlowSOM analysis output within Cytobank.Click the links below to jump to the relevant section: This article outlines the content of static output files from FlowSOM, how to interact with the analysis output within Cytobank, ways to assess FlowSOM quality, and how to perform downstream exploratory data analysis with FlowSOM. As part of the analysis process, you’ll want to assess the quality of the FlowSOM analysis, which can be done by displaying FlowSOM metaclusters on a viSNE map and with heatmaps that show the marker expression of the FlowSOM metaclusters. To learn more about performing SPICE analyses and to get access to all of our advanced materials including 20 training videos, presentations, workbooks, and private group membership, get on Mastery Class wait list.After successfully setting up and running a FlowSOM analysis, you can perform exploratory or quantitative downstream analysis on your FlowSOM results. This is a very simple case, in fact so simple SPICE is not really needed, but it is a good example of where to begin with SPICE, starting from data you already have with minimal manipulation. Use SPICE’s commands on the left to show averages by patient, or overlay comparison of smokers vs non-smokers, or whatever question fits your work. Read the help file for details on formatting data. Be careful with formatting, as SPICE is pretty particular about that. Paste your tabular data into Excel, or really any software that can save your data as a comma separated value file (CSV). Don’t forget to include Keywords! In my case, I would add on a keyword about whether a sample came from a smoker or non-smoker, as well as an identifier.Ĥ. In this simple example you have made 2 gates for CD309+ and AC133+. Use Boolean gates to define all possible subsets. If you are interested in percentage of CD34+ cells that also express AC133 and/or CD309, create gates for your CD34+ population, and individual gates for the dependents. Gate down to your base population of interest. At the individual level, that’s easy and what standard data analysis packages can do.ġ. Take, for example, research interested in CD34+ cell counts. SPICE is an acronym for Simplified Presentation of Incredibly Complex Evaluations, and it is designed to look at these complex multidimensional data sets. You can read the paper about the design and math behind SPICE here. SPICE was developed in order to make sense of the increasingly complex data sets that modern flow cytometric methods can produce. To overcome this limitation, and to allow for better discovery science, Mario Roederer and his colleagues have developed a solution. With data complexity of this nature, one can export the numerical data to a third party analysis package, but even then the analysis can be difficult to perform. What do you do when you have a large dataset, with multiple sampling conditions, and multiple outcome measurements? Even beginners are starting with 5+ color assays, and the adoption of mass cytometry has the potential to increase our headaches even more.Ĭurrent data analysis methods are good for single tubes or small cohort studies. ![]() Gone is the rule of 2-3 color experiments. Flow cytometry data analysis is getting more complex.
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